sklears-feature-extraction 0.2.0

//! Simple neural network feature extractors
//!
//! This module provides placeholder implementations for neural network-based feature extractors.

use scirs2_core::ndarray::{Array2, ArrayView2};
use sklears_core::error::Result as SklResult;

#[derive(Debug, Clone)]
pub struct Autoencoder {
    input_dim: usize,
    encoding_dim: usize,
    n_epochs: usize,
    learning_rate: f64,
    activation: String,
}

#[allow(non_snake_case)] // X follows sklearn/math convention for feature matrix
impl Autoencoder {
    pub fn new() -> Self {
        Self {
            input_dim: 100,
            encoding_dim: 10,
            n_epochs: 100,
            learning_rate: 0.01,
            activation: "relu".to_string(),
        }
    }

    pub fn input_dim(mut self, input_dim: usize) -> Self {
        self.input_dim = input_dim;
        self
    }

    pub fn encoding_dim(mut self, encoding_dim: usize) -> Self {
        self.encoding_dim = encoding_dim;
        self
    }

    pub fn hidden_dims(mut self, hidden_dims: Vec<usize>) -> Self {
        // Use the smallest hidden dimension as the bottleneck encoding size
        if let Some(&dim) = hidden_dims.iter().min() {
            self.encoding_dim = dim;
        }
        self
    }

    pub fn n_epochs(mut self, n_epochs: usize) -> Self {
        self.n_epochs = n_epochs;
        self
    }

    pub fn learning_rate(mut self, learning_rate: f64) -> Self {
        self.learning_rate = learning_rate;
        self
    }

    pub fn activation(mut self, activation: String) -> Self {
        self.activation = activation;
        self
    }

    pub fn fit<T>(&self, _X: &ArrayView2<f64>, _y: &T) -> SklResult<Self> {
        // Placeholder implementation - return a clone
        Ok(self.clone())
    }

    pub fn transform(&self, X: &ArrayView2<f64>) -> SklResult<Array2<f64>> {
        // Placeholder implementation - encode to latent space
        let n_samples = X.nrows();
        Ok(Array2::zeros((n_samples, self.encoding_dim)))
    }

    pub fn inverse_transform(&self, X: &ArrayView2<f64>) -> SklResult<Array2<f64>> {
        // Placeholder implementation - decode from latent space back to input space
        let n_samples = X.nrows();
        Ok(Array2::zeros((n_samples, self.input_dim)))
    }

    pub fn fit_transform(&self, X: &ArrayView2<f64>) -> SklResult<Array2<f64>> {
        // Placeholder implementation
        let n_samples = X.nrows();
        Ok(Array2::zeros((n_samples, self.encoding_dim)))
    }
}

impl Default for Autoencoder {
    fn default() -> Self {
        Self::new()
    }
}

#[derive(Debug, Clone)]
pub struct RestrictedBoltzmannMachine {
    n_components: usize,
    learning_rate: f64,
    batch_size: usize,
    n_iter: usize,
}

#[allow(non_snake_case)] // X follows sklearn/math convention for feature matrix
impl RestrictedBoltzmannMachine {
    pub fn new() -> Self {
        Self {
            n_components: 256,
            learning_rate: 0.1,
            batch_size: 10,
            n_iter: 10,
        }
    }

    pub fn n_components(mut self, n_components: usize) -> Self {
        self.n_components = n_components;
        self
    }

    pub fn learning_rate(mut self, learning_rate: f64) -> Self {
        self.learning_rate = learning_rate;
        self
    }

    pub fn batch_size(mut self, batch_size: usize) -> Self {
        self.batch_size = batch_size;
        self
    }

    pub fn n_iter(mut self, n_iter: usize) -> Self {
        self.n_iter = n_iter;
        self
    }

    pub fn fit_transform(&self, X: &ArrayView2<f64>) -> SklResult<Array2<f64>> {
        // Placeholder implementation
        let n_samples = X.nrows();
        Ok(Array2::zeros((n_samples, self.n_components)))
    }
}

impl Default for RestrictedBoltzmannMachine {
    fn default() -> Self {
        Self::new()
    }
}

#[derive(Debug, Clone)]
pub struct VariationalAutoencoder {
    encoding_dim: usize,
    hidden_dim: usize,
    n_epochs: usize,
    learning_rate: f64,
    beta: f64,
}

#[allow(non_snake_case)] // X follows sklearn/math convention for feature matrix
impl VariationalAutoencoder {
    pub fn new() -> Self {
        Self {
            encoding_dim: 10,
            hidden_dim: 64,
            n_epochs: 100,
            learning_rate: 0.001,
            beta: 1.0,
        }
    }

    pub fn encoding_dim(mut self, encoding_dim: usize) -> Self {
        self.encoding_dim = encoding_dim;
        self
    }

    pub fn input_dim(self, _input_dim: usize) -> Self {
        // For simplicity, we'll just store it but not use it in the placeholder
        self
    }

    pub fn hidden_dim(mut self, hidden_dim: usize) -> Self {
        self.hidden_dim = hidden_dim;
        self
    }

    pub fn n_epochs(mut self, n_epochs: usize) -> Self {
        self.n_epochs = n_epochs;
        self
    }

    pub fn learning_rate(mut self, learning_rate: f64) -> Self {
        self.learning_rate = learning_rate;
        self
    }

    pub fn beta(mut self, beta: f64) -> Self {
        self.beta = beta;
        self
    }

    pub fn fit_transform(&self, X: &ArrayView2<f64>) -> SklResult<Array2<f64>> {
        // Placeholder implementation
        let n_samples = X.nrows();
        Ok(Array2::zeros((n_samples, self.encoding_dim)))
    }
}

impl Default for VariationalAutoencoder {
    fn default() -> Self {
        Self::new()
    }
}